Introduction
Amazon EC2 Instance Metadata Service (IMDS) can help users obtain information about the instance itself. With IMDS, you can access various information about the instance, such as hostname, host IP, temporary access credentials, user data, and more. While this information is convenient for users, it also introduces new risks.
Risks
A common attack scenario is when an instance has an SSRF vulnerability, attackers can retrieve information from the metadata. If temporary access credentials exist in the metadata, attackers can use these credentials for lateral attacks.
Furthermore, if sensitive information is present in user data, attackers can retrieve this data through the metadata, resulting in information leakage and facilitating the next steps of attack and exploitation by the attackers.
Risk Detection
Manual Assessment
There are three main ways to assess the risks:
- Access http://169.254.169.254/latest/meta-data/ within the instance to check if the returned result is normal. If it is, it indicates that the instance's metadata is enabled and accessible without authorization.
- In the instance's Action → Instance settings → Modify instance metadata options, if the Instance metadata service is in an "Enable" state and IMDSv2 is in an "Optional" state, it means the metadata is at risk.
- You can also use the AWS CLI to obtain the current instance's state:
F!
aws ec2 describe-instances --instance-ids <instance_id> --query 'Reservations[*].Instances[*].MetadataOptions'
If the HttpTokens in the returned content is "optional," it indicates that the metadata service of the current instance is at risk.
[
[
{
"State": "applied",
"HttpTokens": "optional",
"HttpPutResponseHopLimit": 1,
"HttpEndpoint": "enabled",
"HttpProtocolIpv6": "disabled",
"InstanceMetadataTagsF!": "disabled"
}
]
]
Using Selefra for Assessment
Manual discovery of these issues can be time-consuming and cannot be done in bulk. Using Selefra can help you quickly identify these risks. Selefra is a tool for rapidly discovering multi-cloud and SaaS risks. The Selefra project can be found at github.com/selefra/selefra.
Let's start by installing Selefra:
brew tap selefra/tap
brew install selefra/tap/selefra
Next, create a project folder:
mkdir selefra-test
cd selefra-test
Copy the following YAML file to this folder:
selefra:
name: selefra-test
connection:
type: postgres
username: your_username
password: your_password
host: 127.0.0.1
port: 5432
database: postgres
sslmode: disable
log_level: info
providers:
- name: aws
source: aws
version: v0.1.0
providers:
- name: aws
provider: aws
cache: 7d
rules:
- name: ec2_metadata_unlimited_access
metadata:
title: EC2 metadata unlimited access
severity: High
query: |-
SELECT
*
FROM
aws_ec2_instances
WHERE
metadata_options ->> 'HttpTokens' = 'optional';
output: "EC2 metadata unlimited access, arn: { {.arn} }"
As you can see, it is divided into three modules: selefra, provider, and rules.
In the selefra module, configure your own PostgreSQL database connection address, username, and password in the connection block. The cache block in the provider module can set the cache time for fetching data. The rules module is for configuring the detection issues. The title block represents the title of the detection strategy, and the SQL statement in the query block is used to execute the detection strategy by querying the vulnerable resources in the database.
Before starting the detection, configure your AWS credentials by running the following command:
aws configure
Then run the following command to run Selefra:
selefra apply
Now Selefra will start the detection process. Below is an example of the result:
In the result, we can see that there are 3 instances with metadata risks.
In addition to the above method, Selefra also integrates GPT functionality. This feature allows you to discover risk points by directly querying Selefra.
Using Selefra GPT for Assessment
Similar to the previous steps, create a new folder and copy the following YAML file into it:
selefra:
name: selefra-test
connection:
type: postgres
username: yourusername
password: yourpassword
host: 127.0.0.1
port: 5432
database: postgres
sslmode: disable
log_level: info
openai_api_key: your_openai_api_key
openai_mode: gpt-4
openai_limit: 10
providers:
- name: aws
source: aws
version: v0.1.0
providers:
- name: aws
provider: aws
cache: 30d
rules:
The difference here is that you need to provide your own OpenAI API Key. You can also set whether to use GPT-4 or GPT-3.5 in the openai_mode. Furthermore, leave the rules block empty, as AI will generate the content for this section.
Before starting the detection, configure your AWS credentials as before. Then you can use the GPT functionality:
selefra gpt "Query instances with unrestricted access to metadata."
**
**
As you can see, with just a single sentence, you can discover instances where the metadata service is at risk. It's very convenient.
Prevention
To mitigate the risks associated with the AWS EC2 Instance Metadata Service, there are two main methods. First, if you don't need metadata, you can disable it directly. Second, if you need to use the metadata service, you can enable token-based access to prevent direct access to metadata.
To disable the metadata service, there are three common methods:
1.Disable metadata during instance creation.
2.After creating the instance, go to the console's Action → Instance settings → Modify instance metadata options and disable it.
After creating the instance, you can also use AWS CLI to disable it:
aws ec2 modify-instance-metadata-options --instance-id <instance_id> --http-endpoint disabled
To enable token-based access, there are also three common methods:
1.During instance creation, select "V2 only" in the Metadata version.
2.After creating the instance, go to the console's Action → Instance settings → Modify instance metadata options, and check "Required" for IMDSv2.
After creating the instance, you can also use the command line to modify it:
aws ec2 modify-instance-metadata-options --instance-id <instance_id> --http-tokens required
Conclusion
In this article, we introduced the risks associated with metadata and how to mitigate these risks. By using Selefra, you can quickly and efficiently identify instances that have metadata risks. We hope this article has been helpful to you and that Selefra can make your cloud environment more secure.
GitHub: https://github.com/selefra/selefra
Slack: https://selefra.io/community/join
Recommended reading
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